Pose-Self-Supervised Style Transfer for Weakly Paired Images
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Solution Overview
Problem
Existing image style transfer methods struggle to maintain the original pose of an image during style transfer, especially when dealing with weakly paired data, leading to entanglement of style, pose, and content, and failing to exploit the information of bounded relative poses.
Innovation Solution
A pose self-supervised generative adversarial network (PREGAN) is employed to learn a network Nt from weakly paired data, using pose injection and recovery as domain randomization and self-supervision to ensure pose-insensitive style learning, with a total loss function that minimizes the difference between generated and target data distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unpaired image style transfer is used to handle realistic robotic applications, then adaptability to different scenarios is improved, but manufacturing precision of style transfer deteriorates due to blurred boundary between content and style
Solution Approach 1:
The patent applies local quality by using pose-guided attention mechanisms that selectively focus on specific regions of the image corresponding to style elements. The attention map is generated based on pose information, allowing the network to precisely control which local regions undergo style transfer while preserving content structure, thus achieving both adaptability and precision
Solution Approach 2:
The patent changes the parameter space by introducing pose parameters (rotation angles, translation vectors) as additional inputs to the style transfer network. This allows the model to condition the style transfer process on geometric transformations, enabling precise control over style application while adapting to different viewing angles and positions
2Manufacturing precision
If supervised paired image transfer is used to maintain precision, then manufacturing precision of style transfer is improved, but adaptability to realistic robotic applications deteriorates due to difficulty in obtaining perfectly paired data
Solution Approach 1:
The patent introduces pose information as an intermediary element that mediates between unpaired source and target images. The pose parameters serve as a bridge that provides structural guidance for style transfer without requiring pixel-perfect alignment, enabling the model to leverage weakly paired data while maintaining precision through the intermediate pose representation
Solution Approach 2:
The patent performs preliminary pose estimation and alignment before the actual style transfer process. By pre-computing pose parameters from the input images and using them to guide the attention mechanism, the model prepares the transformation in advance, allowing precise style transfer even when complete pixel-level pairing is unavailable
3Adaptability or versatility
If cycle consistency is extensively used in unpaired style transfer, then adaptability is improved, but device complexity increases due to training difficulties
Solution Approach 1:
The patent extracts and isolates the pose estimation task from the overall style transfer process. By using a separate pose estimation module that operates independently and provides guidance to the style transfer network, the model avoids the complexity of implementing full cycle consistency while maintaining adaptability through the extracted pose information
Data Source
AI summary
A weakly paired image style transfer method based on a pose self-supervised generative adversarial network, relating to the field of image processing. The method is suitable for style transfer of weakly paired images, different styles of pictures having certain overlap are used to perform model training of an adversarial neural network, so that the model is insensitive to poses and focuses on style learning, and therefore, in an actual application process, a source style can be converted into a target style, but a pose is kept unchanged. In addition, in the model training process of the adversarial neural network, a differentiable pose solver capable of estimating a relative pose of any two images is introduced, a phase correlation algorithm is optimized to be differentiable, and the phase correlation algorithm is embedded into an end-to-end learning network framework to achieve pose estimation.